reinforcement learning

Editorial: The role of cellular subtypes in striatal activity

The basal ganglia are central to the selection and execution of actions, integrating diverse information to shape motor, cognitive, and motivational behaviors. Within this network, the striatum occupies a key position where convergent cortical, thalamic, and neuromodulatory inputs are transformed into patterns of activ...

A hierarchical reinforcement learning framework for high-efficiency control of turbine-assisted fuel cell air supply systems

Event-Based Reinforcement Learning for Cyber-Physical Energy Systems – Smart Buildings, Smart Grid, and Smart Cities

DiffLyap-MEC: Diffusion-Guided Lyapunov Optimization for Joint Offloading, Semantic Compression, and Resource Allocation in ISAC-Enabled 6G Edge Computing

Integrated sensing and communication (ISAC), semantic communication, and mobile edge computing (MEC) jointly create a high-dimensional, non-convex, and time-coupled resource allocation problem in sixth-generation (6G) networks that existing heuristics and deep reinforcement learning (DRL) methods solve either slowly or...

ENGINEERING DECISION SYSTEMS FOR METAL FABRICATION: VYACHESLAV SHARGAEV'S APPLIED AI APPROACH

Closed-loop artificial intelligence is increasingly claimed for metal-fabrication systems that combine reinforcement-learning-based process control with vision-based quality inspection. This review organizes the reinforcement-learning, computer-vision, and production-economics literature bearing on metal cutting around...

Topology-aware graph reinforcement learning for generalizable cascading failure mitigation in hybrid AC/DC power systems

Cascading failures in hybrid AC/DC power systems can propagate rapidly through DC-side disturbances, AC branch overloads, topology changes, and islanding, making emergency mitigation a constrained sequential decision-making problem under evolving network structures. This paper proposes a topology-aware graph reinforcem...

Hybrid AC/DC Distribution Network Planning Under Renewable Ramp Stress Considering Network-Constrained Flexibility via RL with Feasible Projection

High renewable energy penetration intensifies net-load ramping and increases the demand for operational flexibility in distribution networks. However, existing hybrid AC/DC distribution network planning studies rarely consider both renewable ramp stress and the network deliverability of available flexibility. Furthermo...

Electronic System-Level Optimization of Network Slicing and Resource Allocation for 6G URLLC Applications

In this paper, we propose a complete mathematical framework for optimizing network slicing and resource allocation at the electronic system-level (ESL) of 6G ultra-reliable low-latency communication (URLLC). We present a multi-dimensional, non-convex mixed-integer stochastic optimization problem that jointly optimizes...

Hybrid Path Planning and Reinforcement Learning-Based Magnetic Guidewire Navigation in Cardiac Intervention

Magnetically driven microrobots have shown significant potential in biomedical applications, including endovascular intervention, targeted therapy, and drug delivery, particularly in the treatment of cardiovascular diseases. Conventional vascular interventional procedures typically rely on physicians to manually manipu...

Adaptive Parallel-World Self-Rewarding and Cross-Model Mutual Rewarding for Large Language Models

Reward generation is a central component of reinforcement learning, preference optimization, and self-improvement in large language models (LLMs). Existing approaches have demonstrated that an LLM can evaluate its own outputs and use the resulting judgments to improve its behavior. However, self-rewarding systems may s...

Adaptive Switching Between Self-Reinforcement and Mutual Reinforcement Learning for Multiple Large Language Models

Large language models (LLMs) can generate candidate answers, evaluate their quality, and use evaluation signals to improve subsequent responses. However, self-evaluation may reinforce a model's own biases and errors, whereas evaluation by other models may introduce evaluator disagreement, capability mismatches, and cor...

Deep-Economic Dispatch: A Deep Reinforcement Learning Framework for Optimal Power Generation Scheduling in Real-Time Electricity Markets

The increasing complexity of real-time electricity markets and fluctuating load demands challenges traditional economic dispatch methods, which often fail to balance cost efficiency and system reliability. This study presents a Deep Reinforcement Learning (DRL) framework for real-time power dispatch, enabling adaptive...